Business Method Using The Automated Processing of Paper and Unstructured Electronic Documents

ABSTRACT

The invention features a business method that takes advantage of the ability to convert unorganized information in the form of paper documents, document images, and electronic documents arid converts the information to an organized electronic form referred to as Knowledge Objects. The invention further encompasses forming electronic Business Objects, such as documents and data sets, useful for business decision making and information exchange. The methods of the invention may utilize computerized storage and computerized decision-making systems to enable making more rapid critical business decisions.

This application claims priority to the U.S. Provisional Application No. 60/730,237 filed Oct. 24, 2005 entitled “A Business Method Using the Automated Processing of Paper Documents” which is hereby incorporated by reference herein in its entirety. All publications and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference.

FIELD OF THE INVENTION

The field of invention is the process or method of doing business by processing paper documents, image files, and/or electronic documents; using a computer to analyze, collate and capture information from the documents; optionally using a computer for making decisions based on this information; and exchanging the organized information between organizations electronically. The field of the invention also includes the method of performing such analysis, collation, and information capture as well as an apparatus for conducting such analysis, collation, and capture.

BACKGROUND OF THE INVENTION

The mortgage banking industry is faced with the daunting task of organizing, inputting and accessing a vast number and array of divergent types of documents and manually entering several hundred fields of information from a subset of these documents in order to make a loan to a borrower. Although many attempts have been made to streamline the process, most recently by the Mortgage Bankers Association (MBA) which established standards for representing information in a mortgage transaction, the problem of identifying and capturing information from paper documents, image files, native PDF files, and other electronic files in the loan origination process has yet to be solved in order to take advantage of these standards. In the United States alone, mortgage bankers are faced with the idiosyncratic documents from a minimum of fifty states where some mortgage documents differ from state to state and may have further individual variations within each state. In addition, once the loan is made to the borrower, there is a huge secondary market for mortgages, where existing mortgage loans are bundled and sold to large investment firms. These investment entities, in order to pursue a rational risk management policy presentable to their owners and/or shareholders, must organize and analyze these mortgage documents for asset risk and compliance with local, state and federal laws. Values necessary to compare and analyze these loans must be extracted from paper documents or images of the document, then tabulated, analyzed and the resultant data and documents made readily available in order for informed decision-making to occur. In January 2000, the MBA formed the Mortgage Industry Standards Maintenance Organization (MISMO). This group has driven the development of industry specifications that allow seamless data exchange using standard electronic mortgage documents called SMART DOcS™. The SMART Doc XML specification is the foundation of the eMortgage efforts of lenders, vendors, and investors, as it provides for the electronic versions of key mortgage documents. This specification enables electronic mortgage loan package creation by providing a standard for creating and processing uniform electronic transactions for use in electronic mortgage commerce.

Nor is this dilemma restricted to the mortgage industry. In other industries, including the finance industry, the hospitality industry, the health care field and the insurance industry, there is a constant need to collate documents into logically related groups, and capture key information to enable information exchange. These documents must be further collated in order to identify and store multiple revisions of the same type of document, along with extracting data and inferred information from the documents, together with making the resultant transaction data and underlying documents available in an electronically accessible manner.

Unfortunately, the manual organization, collation of paper documents, and extraction of information is very time consuming and slows the process of making business decisions. Additionally, there is an increased possibility of error due to manual processing. Validation of these decisions is very difficult since the paper documents are stored separately from the electronic databases maintained by the processing organizations. Thus, there is a clear need for process automation and well organized and easily searchable electronic storage of the documents as well as extraction of relevant information contained within the documents.

In other methods or processes known in the art, automated document identification or classification methods fall into one of three categories: (1) they are either completely dependant on image based techniques for classification; (2) they use simple keyword search techniques, Bayesian and/or Support Vector Machine (“SVM”) algorithms for text classification; or (3) they rely on document boundary detection methods using image and text based classification techniques. These methods are inadequate to deal with the wide variation in documents typically seen in the business environment and are not capable of separating multiple revisions of the same document type to enable information to be captured from the most current version of the document, hence limiting the utility of such systems.

Although it is known in the art to view paper documents by conversion into simpler electronic forms such as PDF files, these files, in general, do not allow extracting information beyond Optical Character Recognition (OCR). The OCR quality is highly dependant on image quality and the extraction is frequently of very poor quality. Finally, these methods or apparatuses do not offer a complete solution to the dilemma of analyzing and manipulating large paper document sets. Thus, the automated systems currently available generally have at least the following problems:

-   (1) such systems are limited to document boundary detection,     document classification and text extraction and do not offer     advanced document collation with separation of very similar     documents, and domain-sensitive scrubbing of extracted information     into usable data; -   (2) techniques based on the current methods of out-of-context     extraction and keyword-based classification cannot offer the     consistent extraction of information from documents for automated     decision making, or formation of Business Objects such as SMART     DocS™ for information exchange between two organizations using     industry standard taxonomy; -   (3) similarity among documents may lead to misclassification when     using pattern-based classification, especially in cases where the     optical character recognition quality of the document is poor; -   (4) extraction processes that handle structured data using a     template-based matching generally fail even with a slight shifting     of images, and those with rules-based templates can return false     results if there are significant variations of the document; -   (5) such systems cannot handle both structured and unstructured     documents equally efficiently and reliably to serve an entire     business process; -   (6) such systems frequently are wed to the strengths and weaknesses     of a particular algorithm and are thus not able to handle wide     variations in analyzed documents with acceptable accuracy without     manual rule creation; -   (7) such systems cannot locate the information across the documents     and variations; -   (8) neither do such systems provide a complete solution to a     business problem; and -   (9) such systems do not have intelligent scrubbing of extracted     information to enable the creation of electronic transaction sets     such as MISMO SMART Doc™ XML files.

To analyze complicated documents, workers in several industries, for example, mortgage banking, currently analyze documents using a manual collation process; a manual stacking process; a wide variety of manual classification methods; and manual extraction methods, in particular a manual search and transcription. These methods suffer from the disadvantages of requiring substantial investment of human capital and not being automated sufficiently to handle bulk processing of documents and the information contained in those documents.

The number and kind of documents accompanying a mortgage loan are very specific to the mortgage loan industry, and as mentioned above, vary from state to state, and may vary in the jurisdictions within a particular state. However, the documents related to a given loan for the purchase of a property or properties in any jurisdiction may be assembled into electronic images by scanning (or direct entry, if already in an electronic form) before, during and after funding of the loan to form a partially, or preferably, complete document set, referred to herein as the “Dox Package.” These documents originate from a number of sources, including banks and/or credit unions. Moreover, the order of these documents are assembled and filed depends very much on the individuals involved, their timeliness and their preferences, organization, or disorganization in sorting the various forms and other documents containing the required information. Further, even though some standardization of documents has occurred, such as Form 1003 published by FNMA, certain data essential for further analysis may still be found at disparate locations in idiosyncratic documents. For example, each bank and credit union formats an individual's bank statement in a different manner, yet the data from each format must be extracted for income verification. Additionally, depending on the stage of loan processing, not all of the documents may be present in a Dox Package at a given point in time.

As mentioned above, following the funding of the loan, loans are frequently bundled with many other similar loans and sold on the secondary market. At this stage, entire lots of mortgage-secured loans are bundled and sold with minimal quality control. In current usage in the secondary mortgage market, a randomly selected ten percent sample of mortgage documents (Dox Packages) are analyzed in detail (largely by manual means) and taken as representative for the lot. Obviously, if more loans, or substantially all the loans in a bundle, could be evaluated, better decisions could be made regarding the marketing of mortgage-backed loans on the secondary market. Hence, pricing of these loans in the market would be more efficient. Thus, there is a clear need for the automated analysis, collation of documents, and extraction of information in the mortgage loan industry, as well as other industries with no automated or standardized data input in place.

The following patents and applications may also be relevant in describing the background of the instant invention: U.S. patent application No. 2005 0134935 and U.S. Pat. Nos.: U.S. Pat. No. 6,754,389, U.S. Pat. No. 6,751,614, U.S. Pat. No. 6,742,003, U.S. Pat. No. 6,735,347, U.S. Pat. No. 6,732,090, U.S. Pat. No. 6,728,690, U.S. Pat. No. 6,728,689, U.S. Pat. No. 6,718,333, U.S. Pat. No. 6,704,449, U.S. Pat. No. 6,701,305, U.S. Pat. No. 6,691,108, U.S. Pat. No. 6,675,159, U.S. Pat. No. 6,668,256, U.S. Pat. No. 6,658,151, U.S. Pat. No. 6,647,534, U.S. Pat. No. 6,640,224, U.S. Pat. No. 6,625,312, U.S. Pat. No. 6,622,134, U.S. Pat. No. 6,618,717, U.S. Pat. No. 6,611,825, U.S. Pat. No. 6,606,623, U.S. Pat. No. 6,606,620, U.S. Pat. No. 6,604,875, U.S. Pat. No. 6,604,099, U.S. Pat. No. 6,592,627, U.S. Pat. No. 6,585,163, U.S. Pat. No. 6,556,987, U.S. Pat. No. 6,556,982, U.S. Pat. No. 6,553,365, U.S. Pat. No. 6,553,358, U.S. Pat. No. 6,542,635, U.S. Pat. No. 6,519,362, U.S. Pat. No. 6,512,850, U.S. Pat. No. 6,505,195, U.S. Pat. No. 6,502,081, U.S. Pat. No. 6,499,665, U.S. Pat. No. 6,487,545, U.S. Pat. No. 6,477,528, U.S. Pat. No. 6,473,757, U.S. Pat. No. 6,473,730, U.S. Pat. No. 6,470,362, U.S. Pat. No. 6,470,307, U.S. Pat. No. 6,470,095, U.S. Pat. No. 6,460,034, U.S. Pat. No. 6,457,026, U.S. Pat. No. 6,442,555, U.S. Pat. No. 6,411,974, U.S. Pat. No. 6,397,215, U.S. Pat. No. 6,362,837, U.S. Pat. No. 6,353,827, U.S. Pat. No. 6,298,351, U.S. Pat. No. 6,289,337, U.S. Pat. No. 6,266,656, U.S. Pat. No. 6,259,812, U.S. Pat. No. 6,243,723, U.S. Pat. No. 6,233,575, U.S. Pat. No. 6,216,134, U.S. Pat. No. 6,212,517, U.S. Pat. No. 6,199,034, U.S. Pat. No. 6,185,576, U.S. Pat. No. 6,185,550, U.S. Pat. No. 6,178,417, U.S. Pat. No. 6,175,844, U.S. Pat. No. 6,157,738, U.S. Pat. No. 6,128,613, U.S. Pat. No. 6,125,362, U.S. Pat. No. 6,101,515, U.S. Pat. No. 6,094,653, U.S. Pat. No. 6,088,692, U.S. Pat. No. 6,061,675, U.S. Pat. No. 6,055,540, U.S. Pat. No. 6,044,375, U.S. Pat. No. 6,038,560, U.S. Pat. No. 5,999,893, U.S. Pat. No. 5,999,647, U.S. Pat. No. 5,995,659, U.S. Pat. No. 5,991,709, U.S. Pat. No. 5,983,246, U.S. Pat. No. 5,966,652, U.S. Pat. No. 5,960,383, U.S. Pat. No. 5,943,669, U.S. Pat. No. 5,940,821, U.S. Pat. No. 5,937,084, U.S. Pat. No. 5,930,788, U.S. Pat. No. 5,918,236, U.S. Pat. No. 5,909,510, U.S. Pat. No. 5,907,821, U.S. Pat. No. 5,905,991, U.S. Pat. No. 5,873,056, U.S. Pat. No. 5,867,799, U.S. Pat. No. 5,854,855, U.S. Pat. No. 5,848,186, U.S. Pat. No. 5,835,638, U.S. Pat. No. 5,832,470, U.S. Pat. No. 5,819,295, U.S. Pat. No. 5,812,995, U.S. Pat. No. 5,794,236, U.S. Pat. No. 5,768,580, U.S. Pat. No. 5,717,913, U.S. Pat. No. 5,706,497, U.S. Pat. No. 5,696,841, U.S. Pat. No. 5,694,523, U.S. Pat. No. 5,689,342, U.S. Pat. No. 5,598,557, U.S. Pat. No. 5,588,149, U.S. Pat. No. 5,579,519, U.S. Pat. No. 5,574,802, U.S. Pat. No. 5,568,640, U.S. Pat. No. 5,535,382, U.S. Pat. No. 5,519,608, U.S. Pat. No. 5,500,796, U.S. Pat. No. 5,479,574, U.S. Pat. No. 5,463,773, U.S. Pat. No. 5,428,778, U.S. Pat. No. 5,426,700, U.S. Pat. No. 5,423,032, U.S. Pat. No. 5,418,946, U.S. Pat. No. 5,414,781, U.S. Pat. No. 5,323,311, U.S. Pat. No. 5,297,042, U.S. Pat. No. 5,287,278, U.S. Pat. No. 5,204,812, U.S. Pat. No. 5,181,259, U.S. Pat. No. 5,168,565, U.S. Pat. No. 5,159,667, U.S. Pat. No. 5,107,419, U.S. Pat. No. 5,091,964, U.S. Pat. No. 5,051,891, U.S. Pat. No. 5,048,099, U.S. Pat. No. 5,021,989, U.S. Pat. No. 5,020,019, U.S. Pat. No. 4,899,299, U.S. Pat. No. 4,860,203, and U.S. Pat. No. 4,856,074.

SUMMARY OF THE INVENTION

In one aspect the instant invention features a method of doing business by processing a Dox Packages wherein each Dox Package has at least two pages wherein minimal human intervention is involved in the extraction of information and/or data. In preferred embodiments, the Dox Package has documents related to a mortgage.

In another aspect the instant invention features a method of doing business by processing a group of Dox Packages wherein each Dox Package has at least two pages wherein the information is extracted from the Dox Packages and organized ten times as fast as a human operator. In preferred embodiments, the Dox Package has documents related to a mortgage.

In one aspect, the instant invention features a method of doing business by processing a group of documents, i.e., a Dox Package, where the process comprises some or all of the following steps:

-   (1) providing at least two of the discrete documents pages     containing one or more fields from the group of documents to a     device that can provide optical character recognition (OCR), and     performing optical character recognition from the discrete documents     using the device to generate one or more sets of text-based     information; -   (2) classifying at least some of the discrete document pages using     the sets of text-based information, wherein multiple classification     engines are employed and classification is based on a consensus of     the classification engines, i.e. their vote; -   (3) classifying at least some of the discrete document pages using     Image Based Classification (as defined herein); -   (4) verifying any of the remaining discrete document pages that are     not classified in the step of classifying by employing a Location     Diagram wherein the Location Diagram may be constructed using     Feature Vectors with the remaining discrete document pages or a     portion thereof; -   (5) collating at least two of the discrete document pages that form     discrete documents; -   (6) determining the version number of each document and verifying     the page sequence to form a unique document with a specific     revision/version identity; -   (7) extracting data from the fields of a discrete document to     generate extracted data; -   (8) scrubbing values from the extracted data to generate values     therefrom; -   (9) outputting the values to a data warehouse such as a data storage     device or a hard drive; -   (10) displaying at least some of the values to a user; -   (11) forming required relationships between extracted information to     form Knowledge Objects; and -   (12) collating Knowledge Objects to form Business Objects such as     MISMO SMART Docs.

In one aspect, the instant invention features a method of doing business by processing a group of documents using a computer where the process comprises some or all of the following steps:

-   (1) providing at least two of the discrete documents pages     containing one or more fields from the group of documents to a     device that can provide optical character recognition (OCR), and     performing optical character recognition from the discrete documents     using the device to generate one or more sets of text-based     information; -   (2) classifying at least some of the discrete document pages using     the sets of text-based information, wherein multiple classification     engines are employed and classification is based on a consensus of     the classification engines, i.e. their vote; -   (3) classifying at least some of the discrete document pages using     Image Based Classification; -   (4) verifying any of the remaining discrete document pages that are     not classified in the step of classifying by employing a Location     Diagram wherein the Location Diagram may be constructed using     Feature Vectors with the remaining discrete document pages or a     portion thereof; -   (5) collating at least two of the discrete document pages that form     discrete documents; -   (6) determining the version number of each document and verifying     the page sequence to form a unique document with a specific     revision/version identity; -   (7) extracting data from the fields of a discrete document to     generate extracted data; -   (8) scrubbing values from the extracted data to generate values     therefrom; -   (9) outputting the values to a data warehouse such as a data storage     device or a hard drive; -   (10) displaying at least some of the values to a user; -   (11) forming required relationships between extracted information to     form Knowledge Objects; and -   (12) collating Knowledge Objects to form Business Objects such as     MISMO SMART Docs.

In one aspect, the instant invention features an apparatus for analyzing a group of documents using the methods described herein wherein said apparatus comprises a computer. In this aspect, the instant invention features an apparatus for processing a group of documents where the apparatus performs all or some of the following steps:

-   (1) providing at least two discrete documents pages containing one     or more fields from the group of documents to a device that can     provide optical character recognition (OCR), and performing optical     character recognition from the discrete documents using the device     to generate one or more sets of text-based information; -   (2) classifying at least some of the discrete document pages using     the sets of text-based information, wherein multiple classification     engines are employed and classification is based on a consensus of     the classification engines, i.e. their vote; -   (3) classifying at least some of the discrete document pages using     Image Based Classification; -   (4) verifying any of the remaining discrete document pages that are     not classified in the step of classifying by employing a Location     Diagram wherein the Location Diagram may be constructed using     Feature Vectors with the remaining discrete document pages or a     portion thereof; -   (5) collating at least two of said discrete document pages that form     discrete documents; -   (6) determining the version number of each document and verifying     the page sequence to form a unique document with a specific     revision/version identity; -   (7) extracting data from the fields of a discrete document to     generate extracted data; -   (8) scrubbing values from the extracted data to generate values     therefrom; -   (9) outputting the values to a data warehouse such as a data storage     device or a hard drive; -   (10) displaying at least some of the values to a user; -   (11) forming required relationships between extracted information to     form Knowledge Objects; and -   (12) collating Knowledge Objects to form Business Objects such as     MISMO SMART Docs.

In a still other aspect, the instant invention features a method of analyzing a bundle of loans assembled for sale on the secondary market wherein over 30%, over 40%, over 50%, over 60%, or over 70% of the mortgage documents are analyzed and the data/information is extracted.

In certain embodiments in any of the aspects of the instant invention, ambiguities in the processing of the documents are escalated to a human collaborator, in particular this may occur during or following the classification step, the field location step, and/or the data extraction step. In one embodiment of the instant invention, the step of performing optical character recognition is performed by, or with the assistance of, a computer. In another embodiment of the instant invention, the step of classifying is performed by, or with the assistance of, a computer. In still another embodiment of the instant invention, the step of verifying is performed by, or with the assistance of, a computer. In a further embodiment of the instant invention, the step of collating is performed by, or with the assistance of, a computer. In a still further embodiment of the instant invention, the step of extracting data is performed by, or with the assistance of, a computer. In another embodiment of the instant invention, the step of scrubbing is performed by, or with the assistance of, a computer. In still another embodiment of the instant invention, the outputting is performed by, or with the assistance of, a computer. In still a further embodiment of the instant invention, the step of displaying is performed by, or with the assistance of, a computer. In one embodiment of the instant invention, ambiguities at any step are escalated to a human operator. In another embodiment of the instant invention, the group of documents being analyzed is a group of mortgage loan documents. In other embodiments of the instant invention, the groups of documents being analyzed may be home appraisals, credit reports, and a single loan file where it is frequently used for underwriting purposes.

In any of the above aspects, the invention also features a method of operating a business where a purpose of the business is to offer the method/apparatus of preferred embodiments of the instant invention as a service. In another aspect, the instant invention features advertising the method/apparatus of the instant invention and/or advertising the availability of a service featuring the method/apparatus of the instant invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity in the specification, drawings and figures and in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

FIG. 1 depicts an overview of the business methods of the instant invention.

FIG. 2 depicts a detailed diagrammatic view of the business methods of the instant invention, i.e., the system flow of a preferred embodiment of the invention.

FIG. 3 depicts an embodiment of the Document Learner process, i.e., the flow of the classification learner.

FIG. 4 depicts an embodiment of the Business Object formation elements.

FIG. 4A depicts an embodiment of the relationship of Knowledge Objects within a Business Object.

FIG. 4B depicts the process of Dox Package creation in one embodiment of the invention.

FIG. 4C depicts the process of document creation in one embodiment of the invention.

FIG. 4D depicts MISMO transaction data-set creation in one embodiment of the invention.

FIGS. 5A and 5B depict screen shots of output obtained through the use of the instant invention. That is, using a Dox Package analyzed by the method/apparatus as described herein, the exemplary data in the figure was available for analysis.

DETAILED DESCRIPTION OF THE INVENTION

While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Workers in a variety of organizations and/or industries, such as the mortgage industry, especially the secondary market for the re-sale of mortgage loans, face the enormous problem of tracking a vast array of information presented to them in the form of paper documents arriving in a bewildering array of formats, and require that information transferred to an electronic form for rapid analysis and decision-making. Extracting exact data and/or information from idiosyncratic document sets with accuracy is essential for the data to be useful for decision-making.

As noted above, the MBA formed the Mortgage Industry Standards Maintenance Organization (MISMO) to address this problem. This group has driven the development of industry specifications that allow seamless data exchange using standard electronic mortgage documents called SMART DocS™. However, in order for the mortgage industry to fully utilize this standardization, every piece of software in the industry would have to be re-created to generate data to adhere to this standard. Hence, the industry requires a practical solution to enjoy the increased velocity and standardization that SMART Doc XML standards bring to the loan origination process using the current forms of data available such as paper images, and native PDF files. In a preferred embodiment, it is one of the objects of our invention to provide such a solution.

It is always difficult and time-consuming to determine the exact nature and identity of documents present in such a document set. For example, with reference to the mortgage industry, mortgage documents in some states, e.g., California, contain reports concerning the seismic environment of the subject property. In other states, such documentation might be rarely, if ever, be found in the package of documents associated with the sale of property, or the refinancing thereof (such a document package is referred to herein as the “Dox Package”). Further, without knowing the type of document or specific revision of the document being reviewed, up until now, it has been difficult or impossible to extract the required information from it by automated means. The exact documents provided in a Dox Package may prove insufficient because at a particular point in time, not all required pages of the documents may be available. Additionally, there may be a confusing variety or subvarieties for any given type of document, and further, essential information may be scattered across many or all the pages in the Dox Package. And for added complication, individual pages may arrive in a scrambled order in any given packet, and portions of the packet may arrive for analysis at different times. Obtaining accurate information in an organized form is the challenge solved by the instant invention. If a human were to enter the information into a computer, the process would be labor intensive and would be expected to take much longer. In preferred embodiments, a Dox Package may consist of at least two pages, at least three pages, at least five pages, at least ten pages, at least twenty pages, at least fifty pages, at least one hundred pages, or more, Further, as used herein, a Dox Package includes sets of documents in which all the information/data contained therein is not readily available in electronic or digital forms. Thus, a Dox Package may consist of a variety of documents some of which are electronic documents but some of which are paper copies only, or images, such as PDFs or TIFFs, of such paper documents.

The instant invention, in some embodiments, can extract the information from the heterogeneous set of documents that forms Dox Package and enter that information into a computer database much faster than, and in some embodiments, with minimal or no intervention from, a human operator; in some cases ten times as fast, twenty times as fast, thirty times as fast, forty times as fast, fifty times as fast, or more. Additionally, in one embodiment, the instant invention can extract and enter information from a Dox Package with human review of, at most, one page in ten, one page in twenty, one page in thirty, one page in forty, one page in fifty, one page in sixty, one page in seventy, one page in eighty, one page in ninety, one page in one hundred, or one page in over a hundred.

As used herein, a “Knowledge Object” is a matrix of the information and its association with reference to a particular business process. When a Knowledge Object is not specific to a process and/or a complete domain, it can be cluster of information. Knowledge Objects are intended to be useful and available for decision-making.

The term “Knowledge Object,” as used herein, refers to a set of facts preferably along with their relationship and association with other Knowledge Objects in a given Dox Package. Knowledge Object is a matrix of relevant information entities such as facts, image field coordinates, value type, intended to address and assist decision making in businesses.

As used herein, a “Business Object” is a collected and organized set of information extracted from a Dox Package intended for a business purpose and ready to use to illustrate relationships and/or the utility of Knowledge Objects. It gives a business-centered view of the extracted and organized knowledge for the decision-making process. An example of a Business Object is a MISMO standard SMART Doc

As used herein, the term “Dox Package” refers to the pile, stack, or file of documents that is delivered, handed, and/or made available to the operator of the instant invention. In certain preferred embodiments, the Dox Package comprises mortgage documents and documents in support of a mortgage, or secondary financing thereof.

As used herein, “Taxonomy” refers list of document types (or document classes) expected in any Dox Package.

Documents within the Dox Package or taxonomy may consist of multiple pages, but all pages are preferably logically related to the reference page (as defined below).

The term “escalation” as used herein refers to a subroutine within the method/apparatus in embodiments of the instant invention that when the method/apparatus finds a document and/or page it cannot assign or identify, it escalates the document and/or page out of the program, or automated document analysis, and displays the document to a human collaborator. In preferred embodiments, the page is displayed on a split screen with the “heading region” of the document page amplified at the top of the screen and the entirety of the document shown in the bottom of the split screen. The instant inventors have determined that the identity of most documents can be determined by clues obtainable in the header region.

As used herein, the term “buckets” is a location to store related pages during the processing involved in preferred embodiments of the instant invention. Buckets may later be correlated and classified to the operative taxonomy so that a given bucket becomes a document within the taxonomy system.

The term “forensic page analysis” as used herein refers to a detailed extraction and mapping of the image that forms a sheet or an image of a sheet wherein this mapping is used to identify the page and/or sheet. Forensic page analysis generates a Location Diagram and Feature Vectors.

As used herein, the term “reference page” refers to the most readily identifiable document in a set of documents or pages within a Dox Package. Frequently, it is the first page of a document, but that is not required by the definition as the first page of a document may be a cover page, such as a fax cover page. An example of a reference page is the front page of a Form 1003. The “reference page” herein is the page of a document that represents the maximum logical properties or identifying properties of the document with all subsequent document members able to be classified as having affinity towards this “reference page.” This “reference page” could be, but is not necessarily, the first page of the document within a bucket or with the classified documents.

As used herein, the term “field” refers to the region of a document where specific items of information might be found. Thus, on a Form 1003 there is a field for a name where an individual's name is found; the individual's name is a “fact” and may also referred to herein as a “text snippet” when the fact is extracted from a field.

Thus, fields are converted into facts by extracting the information and “scrubbing” the text output to create a value that can be utilized and/or consumed by a computer in the operation of embodiments of the instant invention.

As used herein, the term “information fields” refers to the content of the blanks on the forms, e.g., in the context of the mortgage field, the price of the property, the amount financed, the address, etc. or specific content from an unstructured document such as stated interest rate in a promissory note.

The term “Feature Vector” as used herein refers to a manner of mapping documents wherein the relationship of keywords to fields or keywords to other keywords is mapped both as to physical distance and direction.

The meaning of the term “Location Diagram” as used herein is best explained by an example. Each file is present in three formats: (1) the original tiff image format, (2) the text format from simple OCR output, and (3) a grid format, i.e., a text pictorial representation of the document. All three formats are used in classification and extraction.

Assuming that A, B, C, D and E are five phrases, the overall representation that may come in a single feature-vector may be represented as follows:

-   -   (1) A and B form a meaning X;     -   (2) A is primary key;     -   (3) B is p columns and q rows away from A;     -   (4) with similar information about other key phrases being         recorded.

These overall positions form a Location Diagram.

Here, the Location Diagram is a relative position map of key phrases represented in unique way by their vectors of relative distances. The structured files are represented in flexible structure maps called grid files.

Collation is done to segregate documents in groups to represent: (1) the Class-version, (2) the document identity (doc id), (3) page, and (4) versions and/or occurrences.

As used herein the term “collate” refers to the process of taking a bucket comprising a document, or a pages of a document, or sheets classified to the same taxonomy identified niche; analyzing the sheets located therein, preferably as well as all the sheets in a Dox Package, and sorting them into the correct buckets whereby all sheets belong to a document will be correctly sorted, and preferably different versions or dates of documents collected together. Thus, the term's definition comprises the dictionary meaning of “collates” whereby a collation occurs through a process that assembles pages in their proper numerical or logical sequence, and/or through a process examines gathered sheets in order to arrange them in the proper sequence. Collation also refers to the process of organizing Knowledge Objects into Business Objects.

OCR is generally referred to as the process of recognizing characters on an image file and converting them to ASCII text characters format.

As used herein, the acronym “NLP” refers to natural language processing, as is known to one of skill in the art.

As used herein, the term “Image Based Classification” refers to methods to classify documents using features and/or references other than text such as the visual page layout, the white-space distribution, and graphic patterns.

The purposes of instant invention include conducting a business and making business decisions using an automated acquisition and analysis of information from a Dox Package. This invention thus, in part, provides:

-   (1) a comprehensive method/apparatus that extracts relevant     information from electronic images of paper documents to electronic     data and assembles the extracted information with a very high level     of accuracy and very little human intervention; -   (2) a comprehensive method/apparatus that facilitates decisions at     all levels by those with an interest in the documents or data     therein by providing data with a quantifiable level of accuracy; -   (3) a comprehensive method/apparatus for classification, collation,     and identifying the version of documents together with relevant     information extraction where the overall method/apparatus being     enabled by an automatic document learner; and/or -   (4) a decision-engineering framework specific to a given business     application to overview and analyze the extracted information. In     preferred embodiments, the documents and/or information may be     converted in an XML file format such as those defined for the     mortgage industry by MISMO.

Although there are a few superficially similar classifiers and extractors in the present-day art, the instant invention has several advantages over the art by fulfilling some or all of the purposes noted above, and in its unique combination of document processing features which include some or all of the following features:

-   (a) it is enabled with automated document learner providing learning     and classification at the level of a page, the level of a zone     within a page, or the level of a field within a page; -   (b) it is easily adaptable to any given business due to its learning     ability; -   (c) it provides incremental learning to allow the system/process to     rapidly accommodate new variations of the same documents as well as     new types of documents; -   (d) it features incremental learning that enables the system to     accommodate variations and adapt to the changes in patterns of     documents; -   (e) it provides validation and verification of located and extracted     information specific to the business domain while minimizing     extraction mistakes and providing a high confidence level in the     accuracy of the results; -   (f) it provides a Location Diagram-based extraction that allows for     accurate extracting of information even with significant changes in     the document formatting; -   (g) it provides, via Location Diagram-based information extraction,     the accurate extraction of information even when page boundary     information is lost during the OCR process, including data slipping     to other pages, and/or the format or organization of the document     changes; -   (h) it provides, via the Location Diagram-based classification and     identification, the ability to provide the sequential number and     order of pages based on intelligence built during learning the     document set in the form of Location Diagrams; -   (i) it provides the ability to separate multiple revisions of the     same document type into unique documents by identifying the     reference page of each document type and the Feature Vector affinity     of associated pages of that document by using distance measurement     algorithms; and -   (j) it provides the ability to further collate the information with     the help of the grid of information created; and -   (k) it provides the ability to flexibly distribute collated     documents or extracted information to a user, or sets of different     documents or information to different users or decision systems     using standards such as MISMO SMARTDocs or custom XML tags.

One of the advantages of embodiments of instant invention is the number of discrete pages it can analyze. Although other document analysis methods and apparatuses exist, the instant invention may handle more pages and more diverse pages than what was present in the art prior to the instant invention. Thus, in embodiments of the instant invention, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 15, 18, 20, 25, 30, 35, 40, or more pages may be analyzed in on Dox Package. Also in embodiments of the instant invention, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 15, 18, 20, 25, 30, 35, 40, or more document types may be analyzed in on Dox Package

Thus, the instant invention provides a method/apparatus that analyzes, and collates documents, even individual versions of similar documents, preferably based on both their logical and their numerical sequence to systematically order groups of pages to enhance usability and to analyze them based on these grouped sets. These grouped sets are meaningful and comprehensive entities and are placed in their unique context for the specific business being supported. This collation takes place in spite of potential extreme variation in documents and in forms and the sequence of the documents or forms being input into the process. In preferred embodiments, the method/apparatus of the instant invention is directed to a specific business, the mortgage loan business, for example.

It is an object of the instant invention to provide comprehensive processes and systems that can convert relevant information from electronic images of papers and/or documents already in an electronic form to an electronic database with minimal human intervention. Further, “Knowledge Objects” are formed based on the extracted information. These Knowledge Objects may be further utilized to form “Business Objects.” The Business Objects are collations of Knowledge Objects centered on specific business requirements and can be used for subsequent decision making. An additional object of the invention is to provide a managing tool that can help in learning and configuring the overall process.

It is also an object of the instant invention to classify documents and uniquely identify documents and revisions of the same document type, and extract information with the aid of automatic learners.

The method/apparatus of the instant invention may collate images of sets of pages for any given type of document package (referred to herein as the “Dox Package”) presented to the operator or the apparatus of the instant invention. It is expected that documents in such a Dox Package may include images of paper documents, such as those in electronic .pdf files, native pdf files, or documents received by fax servers, for example in tiff format. The instant invention, however, is not limited to the handling of such paper documents or images thereof. Thus, as used and defined herein, documents, sets of documents, pages, sets of pages, paper documents, form documents, physical pages, paper form, paper images, sheets, and the like includes documents and the like that exist in digital form, including documents, papers and forms, such as Microsoft® .doc documents and in other proprietary document formats, and the use of such are included within the scope of the present invention. Such documents may also contain embedded images, such as digital signatures or imported graphics or other documents, and likewise are included within the scope of use of the present invention.

In many preferred embodiments of the instant invention, documents are presented or utilized following the OCR conversion of original, signed or executed, documents or a text dump of the native pdf document. Along with mapping to standard MISMO taxonomy, the method/apparatus is also capable of generating its own taxonomy of buckets based on document features observed or recognized by the method/apparatus during analysis of the Dox Package. In this collation process, each page analyzed is assumed to hold a unique position within an individual document, and this page's position is determined and assigned. The method/apparatus initially assigns each page from the Dox Package the most logical bucket and the most appropriate position within the bucket; a page can belong to one and only one logical group. The position or a particular page and the sequence of pages is determined based on the page's purpose, location, readability and usability by the method/apparatus of the instant invention. After being assigned, the location of the sheet or page is preferably repeatedly re-evaluated and thus the accuracy of its position assignment, and the ultimate quality of the data, is increased.

In the case of ambiguity it cannot resolve in the assignment of a document to a bucket or to a page location within a bucket, the method/apparatus of the instant invention, in preferred embodiments, provides for escalation to a human collaborator or assistor to supplement the basic machine and expert-system-based collation. The level of ambiguity that triggers escalation may be preset, modified, or created during operation. In escalation, the human collaborator can determine the identity and classification of the ambiguous document and where it should be assigned to provide clues to the method/apparatus of the instant invention.

The present invention, in preferred embodiments, utilizes Location Diagram concepts and integrates multiple components including image processing, intelligent collation, feedback learning, a document classifier, a verifier, a versioning engine, an information locator, a data extractor, a data scrubber, and manual collaboration. Taking advantage of structured and unstructured properties of documents, the instant invention can convert representations of form documents into grid format, i.e., a text-pictorial representation. Using grid format, the instant invention can extract more and important features from the documents that then can help in formation of a Knowledge Object with very high level of accuracy and minimum human intervention. By using the method/apparatus of some embodiments of the instant invention, human review of pages within the Dox Package may only be required for one page in ten, one page in twenty, one page in thirty, one page in forty, one page in fifty, one page in sixty, one page in seventy, one page in eighty, one page in ninety, one page in one hundred, or less.

(a) Objects of Invention and Their Description

Numerous paper transactions occur in various business fields such as the mortgage industry, the health care field, the various insurance industries, including the health care insurance industry, financial banking, etc. The papers, documents and other information involved in these transactions generally are not random but rather all have interrelationships within a specific business context. Dox Packages obtained during the course of business, or images thereof, typically are not very well organized especially due to the fact that they may be created or obtained by different entities and/or at different points in time. There is need for segregation and subsequent coherent organization of these documents, as well as extracting information from these documents, and organizing and collating the extracted information, e.g. into MISMO standard SMART Doc™, custom XML tag based, other commonly used data file formats, or those to be developed. The need for segregation, organization and collation of documents in the Dox Package arises from a number of reasons: (1) checking for completeness of the Dox Package, i.e., whether all documents required, necessary or desirable to those entities having an interest in the information contained in the Dox Package, are present in the Dox Package; (2) legal aspects of the information contained within the Dox Package; (3) business aspects of the information within the Dox Package, (4) extracting data from a large number of “hard-copy”-only documents or images thereof, which may only be possible from a ‘representative number’ dur to time or money constraints, (5) requiring rapid and inexpensive access to the data contained in the documents for analysis; and (6) having available or distributing documents or sets of documents in a segregated manner based on type of document or other criteria; and (7) making decisions based on the extracted data, including compilations, aggregations, and analyzed or processed sets of such data, optionally with an automated rules engine.

To address these needs and other needs associated with the collation documents and extraction of information, the inventors have devised a method and apparatus to accomplish these tasks to collate and analyze documents and sets of documents, and extract information from specific versions of these documents. The instant invention, in preferred embodiments, provides a comprehensive process and system which can convert information on papers or images to an analyzed and organized electronic form where it can be used for business decision-making.

The present invention, in some embodiments, solves the problems of sorting into versions, sequencing and collating documents and extracting information for specific industries. Thus one object of the instant invention is to provide users with separated, collated and sequenced documents. Users of the instant invention provide the method/apparatus a document set obtained in their course of business, a ‘Dox Package,’ which is then collated and analyzed to meet their business requirements. In preferred embodiments, all documents are provided at once in one location, although such documents may be provided at different times and from different locations. A feature of the invention is that paper documents that do not have all the data contained therein in a segregated digital form are readily used with the instant invention.

This invention, in some preferred embodiments, comprises a comprehensively automated process that can convert data from documents in paper form to electronic form without with little, if any, human intervention. The instant invention may collate and classify documents based on Location Diagrams, which are based on Feature Vectors and connectivity/relationships among them. Further, the engine used in the instant invention can locate and extracts information from documents based on these Location Diagrams with additional scrubbing. The product is equipped with learners, which work on Location Diagram distance maximization within and across the document classes to optimize results, a “studio” (user-friendly interface) and a warehouse for storage and making data available as required by the operator of the instant invention or others designated by the operator. This invention may use methods of solving Location Diagrams based on simultaneous equation- and weight-based confidence measurements. The invention may provide significant benefit to all industries that handle sets of documents, and in particular, large, disparate sets of documents, by accelerating and improving accuracy to current decision-making process when compared to existing and traditional methods/technologies.

The instant invention, in some embodiments, provides a method/apparatus that collates and analyzes a set of documents. The apparatus automatically employs various algorithms to identify groups or logical units of documents. These algorithms work to complement one another to yield higher quality results. Further, the method/apparatus of the instant invention utilizes and takes into account discontinuities, for example, a page break in the middle of a sentence, to assemble pages of a document. Each of these logical units is a complete document identified as to its business identification and mapping to location within the taxonomy.

Further, the invention's method/apparatus preferably measures relatedness among various pages; to accomplish this the method/apparatus works on the principle of a reference page. As used herein, a “reference page” is a page that represents the maximum or near-maximum logical properties of a particular document, and thus all the subsequent document members have affinity towards this reference page. A reference page frequently is, but is not required to be, the first page of a given document. Using the principles of the instant invention, the logical sequence of a Dox Package is related to its purpose, location, readability and usability. Grouping and collating using the principles of the instant invention is concerned with completeness, usability, integrity, and unique occurrence.

The classification and collation unit as used in the instant invention in a preferred embodiment has an Image Based Classifier, a set of text based classifiers, versioning engine, an intelligent collation engine and a verifier. The text based classifier preferably has a set of classification engines and each classification engine confidence is prioritized based on its strengths in handling particular types of documents as will be determined by the particular application and recognized by one of skill in the art operating the instant invention.

The reference page identification method in one embodiment uses a hybrid approach where an affinity determination method is used in connection with an input dictionary, but can also provide feedback to enhance and/or enhance the input dictionary. This dictionary preferably not only provides a list of words but also gives quantitative relevance of words and phrases with reference to each class of document. Keywords and keyphrases have a high affinity towards a given document. For example, word ‘W1’ is defined as having a very high chance of occurrence in document ‘D1’ (e.g., the word ‘interest’ (‘W1’) in a mortgage note (‘D1’)) then, according to the uses and principles of the instant invention, the word ‘W1’ has high affinity towards document ‘D1.’ This affinity may be determined using Bayesian analysis and is represented as a probability or a conditional probability. Other Feature Vectors such as font size and type may also be considered in determining the affinity of a page to the reference page of document being examined. There is no limit to the number of Feature Vectors that might be considered for affinity analysis.

The method/apparatus employs a multi-level approach to identify documents. Typically, the first pass, or Level-1approach identifies some of the reference pages efficiently and quickly. Level-1analysis may identify some reference pages along with their respective classes. Using the instant invention, attempts are made to identify classes for the remaining pages. In preferred embodiments, Level-1uses various statistical algorithms, e.g., algorithms based on SVM and Bayesian. In preferred embodiments, the Level-1reference page identifier is integrated with multi-algorithm classifier which selects the best of set of algorithms based on input data.

These reference pages are mapped to a taxonomy class by measuring the association of Feature Vectors and the relevance of the reference page using supervised learning. The closeness of other pages with reference to reference page is measured. This closeness is used to establish association of these pages with respect to the reference page. The pages in the document are arranged in logical/numerical sequence using this relevance.

The classifier takes advantage of various methods like word phrase frequency, Bayesian analysis, and SVM, but is not limited to these methods and has the capability to give priority and higher weight to the most suitable method to be used for the given document for maximum accuracy and usability.

In some of these preferred embodiments, Location Diagrams and Feature Vectors are neither required nor generated. Documents identified by Level-1algorithms as ambiguous or as having affinity for more than one taxonomy class proceed to Level-2analysis. Thus, all the documents that could not be handled in the Level-1process effectively or routinely by the classifier are sent for verification in a Level-2analysis. The verifier used in the Level-2analysis is preferably capable of resolving the ambiguous document classes leftover from the Level-1analysis. The instant invention also can resolve and relate documents belonging to multi-class families and documents that are within families or a group of classes that are similar. The verifier produces the final identification in these multi-class scenarios, using a combination of voting and critical-feature-based class verification.

In preferred embodiments, with the Level-2analysis, all the documents that are unable to be classified in the Level-1analysis are processed using critical-feature-based verification approach. In other embodiments, all documents are processed using the Level-2analysis. The Location Diagram Map approach used in the Level-2analysis in some preferred embodiments of the instant invention provides the required discrimination and accuracy to handle ambiguous documents and correctly classify or collate them. In preferred embodiments, this Level-2reference page identifier uses critical-feature-based verification and voting, along with the verification algorithm and is referred to as the “verifier.”

In preferred embodiments, the collation process provides for documents to be given a logical and/or numerical sequence. Thus in accordance with the instant invention, a Dox Package is collated with reference to a prescribed or developed taxonomy where the taxonomy classes are characteristic of the industry, (e.g., industry standards like MISMO) or required or desirable by the industry, yet may be adjusted by the operator/user of the instant invention.

Each document and/or page within the Dox Package is mapped to a class according to the taxonomy. The method/apparatus of the instant invention classifies these documents and collates sets of pages for industry standard taxonomy like MISMO, or any given taxonomy. A further feature in some embodiments of the instant invention is that the method/apparatus of the invention is also capable of generating its own taxonomy based on document features it observes. The overall method assigns most logical document structure based on the taxonomy and most appropriate position within each document for each page.

The separation, collation, and sequencing of the documents is taxonomy-based set by users′ business requirements or defined by the field of use, such as MISMO standards, for the documents processed in the instant invention. The initial grouping into buckets and then refined into documents is utilized to further extract information specific to each document and Dox Package and is an important feature of preferred embodiments of the instant invention from the business perspective.

The instant invention may thus assign meanings to the documents and put them in their proper business context by the use of the separation, sequencing and collating methods described above. Each document, group, or subset formed within the Dox Package is based on the document's, group's or subset's use in the relevant business.

In preferred embodiments, the system has human collaboration along with its basic machine learning and expert system based collation.

In preferred embodiments of the instant invention, the method/apparatus of the instant invention is also equipped with a fact extractor for use with the pages, documents or sets of documents in the Dox Package. This fact extraction capability provides for locating and extracting the information/fields required for various business/compliance requirements and transforms the information contained therein to facts or data that can be subject to further use or manipulation. Preferably, the fact extractor is also equipped with weight-based confidence measurement. The fact extractor enables, in part, facts of all types and coming in various forms in the original documents to be accessed, extracted and/or manipulated. As with one feature of the instant invention that ultimately provides for pages, documents or sets of documents to be separated, classified or collated, the fact extraction feature of the instant invention allows for human collaboration for exceptional or problematic documents, although such human intervention is not required. The instant invention can handle all types of fields, e.g., OMR, tables, descriptions, numbers and the like, that will be known to those of skill in the art, depending on the particular business application. The decision system that is optionally used as part of the instant invention provides logical decisions based on this information obtained or extracted and the relevant business context. The preferable need-based human collaboration built into the system makes it possible to extract information and/or data from fields with a very high level of accuracy and coverage.

In preferred embodiments, the instant invention also provides a decision-engineering-framework specific to the business application to organize and utilize the extracted information. Thus, the information extracted from a Dox Package is preferably presented in a usable format, such as a spreadsheet or XML tag file format. Further, automated decisions may be made on the information obtained by an automated rules engine such as Microsoft BizTalk, ILOG jrules, etc.

In an preferred embodiment, an appraisal report regarding a piece of property (most of which are created as PDF files, if they are available electronically at all) and extract the information (including unstructured information), to create an XML output. This output can be used for a variety of purposes such as it may be furnished to a company that evaluates and scores the accuracy risk of the appraiser's information, to generate a report similar to an AVM to a mortgage banker for a business decision. In preferred embodiments, the instant invention may convert the information from an appraisal into electronic data over 100 times as fast as a human operator and with better accuracy..

In another preferred embodiment, the instant invention can extract information automatically from a credit report. This information may be furnished, for example, to a mortgage lender for their risk assessment process.

Thus, in preferred embodiments, the instant invention provides for collation of all the pages, documents, or sets of documents within a Dox Package into a taxonomy classification to meet the business needs of the operator and/or a particular industry. Virtually any Dox Package from any industry may be analyzed by preferred embodiments of the instant invention. Thus, the collated documents are mapped as to a taxonomy such as MISMO or any other industry-specific or user-specific taxonomy. As part or in addition to this, information is extracted from this Dox Package. This information is scrubbed and transformed into discrete data and/or facts. The facts and its related information is used to form an information matrix called a Knowledge Object. The Knowledge Objects are transformed in a particular or required business context to create Business Objects. The Business Objects are then used for business decision-making. In preferred embodiments, the instant invention therefore facilitates extraction of critical information for businesses from the documents, and provides for manipulation, compilation, analyzing and/or access to the facts or data or creation of transaction sets that comply with the MISMO SMART Docs standard and/or other custom XML tag file formats.

Advantages of the Instant Invention

Methods currently available do not meet all the objects of the instant invention, but rather have contributed to the shortcomings, problems and challenges present in the art. Preferred embodiments of the instant invention provide advantages over the current state of the art and these embodiments improve upon them because of all or some of the following reasons:

-   a) the instant invention, in preferred embodiments, offers a     comprehensive process which takes unorganized documents or document     images and yields extracted information suitable for business     decision-making; -   (b) the instant invention, in preferred embodiments, provides for an     automated method with exception-based human collaboration     (“escalation”) to collate with increased speed and accuracy; -   (c) the instant invention, in preferred embodiments, provides     superior accuracy and quantifiable measures for accuracy; -   (d) the instant invention, in preferred embodiments, is the only     comprehensive collation solution which can collate pages, documents,     or sets of documents identified by revision numbers, for business     decision making purposes; -   (e) the instant invention, in preferred embodiments, is not limited     to document separation by boundary detection algorithms; -   (f) the instant invention, in preferred embodiments, provides for     the mapping of documents and document images to a MISMO taxonomy, as     well as other industry standard and custom taxonomies; -   (g) the instant invention, in preferred embodiments, locates and     extracts information from documents and document images sorted into     buckets with a high degree of accuracy; -   (h) the instant invention, in preferred embodiments, provides, among     others, the features of intelligent scrubbing and fact conversion     and/or other data manipulation features; the fact conversion     converts extracted information into data or facts that offer value     to businesses and provide direct input into an automated rules     engine using custom or industry standard XML formats such as those     specified by MISMO; -   (i) the instant invention, in preferred embodiments, provides an     automated learner which can accommodate and incorporate new document     types, and the intelligence to deal with variations in the number     and type of documents and field locations; -   (j) the instant invention, in preferred embodiments, can     incrementally learn to adapt to changes in the patterns between     and/or within documents; -   (k) the instant invention, in preferred embodiments, validates and     verifies collated documents, and Knowledge Objects to improve     accuracy; -   (l) the instant invention, in preferred embodiments, provides a     Location Diagram-based extraction for accurate extractions in case     of slippage, variations and changes in format; and -   (m) the instant invention, in preferred embodiments, features in     some embodiments a collation confidence matrix to be able to assess     the confidence level of the method or algorithm, plus the instant     invention, in preferred embodiments, may effectively use all clues     gathered during all phases of document processing and analysis to     validate the accuracy of the result. Thus, by use of the instant     invention, business decisions, such as whether to invest in a bundle     of loans on the secondary market, may be based on extracted     information from a large number of the associated Dox Packages, or a     majority of the associated Dox Packages, or almost all of the     associated Dox Packages.

One of the advantages of embodiments of instant invention is the number of discrete pages it can analyze. Although other document analysis methods and apparatuses exist, the instant invention may handle more pages and more diverse pages than what was present in the art. Thus, in embodiments of the instant invention, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 15, 18, 20, 25, 30, 35, 40, or more pages may be analyzed in on Dox Package. Also in embodiments of the instant invention, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 15, 18, 20, 25, 30, 35, 40, or more document types may be analyzed in on Dox Package

Illustration of the Instant Invention

As described above, the instant invention is, in preferred embodiments, a process and system for separating, organizing and retrieving information from various documents, for example from a Dox Package. The system preferably employs a collator, a classifier, an extractor, a scrubber, a verifier, a version engine, a voting engine, a transformer for creation of Knowledge Objects and Business Objects, a decision engine and a learner for classification and extraction.

A high-level exemplary overview of one embodiment of the instant invention is provided by FIG. 1 depicting the method/apparatus of the instant invention. Here, unorganized information is captured by the apparatus from various office devices such a computer, a FAX, an e-mail system, a scanner, or uploaded to a FTP or a Web site 101. Further the captured documents or information, unorganized and unidentified when acquired, are organized into an information matrix known as Knowledge Objects by referencing a Knowledge Warehouse 102, and stored in an information data warehouse 103. Knowledge Objects are then transformed into Business Objects, such as electronic documents and transaction sets such as MISMO standard XML files 104. The Business Objects are stored in business data warehouse or delivered to users of the system and external organizations 105. Finally, a Work-flow and Decision engine uses the Business Objects to facilitate both manual and automated business decisions, and collaboration 106.

A detailed exemplary overview of the instant invention is provided by FIG. 2 depicting one preferred embodiment of the method/apparatus of the instant invention. It will be recognized by those of skill in the art that FIG. 2 is only one example or embodiment of the instant invention; other embodiments of the instant invention may be recognized by reference to FIG. 2 and/or the description herein. For example, each of the steps described in FIG. 2 may be modified; further, many of the steps are optional so that one or more steps may be eliminated. Also, other steps may be added. Similarly, the order of the steps may be changed or rearranged in numerous ways. Each of these embodiments is within the spirit and scope of the invention as defined by the appended claims.

Capture Documents:

Pages, documents, sets of documents, a Dox Package, or Dox Packages are sent electronically to the system for classification or/and extraction of data 200. Such documents may be input in any sequence and by or through any manner known to those of skill in the art such as from a fax machine, scanner, e-mail system or any other electronic communication device. The document or documents may be in text, electronic, paper, or image form, or a mixture of formats. If needed, in preferred embodiments, the document(s) are captured by techniques or in a manner known to those of skill in the art. The Dox Package is separated into Image type documents or Text type documents as they are captured.

Image Pre-processing:

The document image quality, in particular, from documents obtained by low resolution scans or facsimile transmission may not be good enough for direct OCR. Therefore, primary image processing may optionally be done to bring the image to the requisite quality for OCR, and Image based classification 202. In preferred embodiments, noise is removed from the image by technologies such as de-skew techniques and de-speckle techniques, a change or changes in DPI, and/or image registration correction or by a combination of the above and/or similar techniques.

Image Based Classification (IBC)

In the preferred embodiment the IBC 203 attempts to identify one or more discrete pages using Image Based Features like lay out, white space distribution, and other features registered in the collection of document feature descriptors by the Document Learner.

OCR (Optical Character Recognition)

If required, as in the case of image type documents, and in preferred embodiments, the portions of or the entire image of a page or document is converted into text using OCR by means known in the art 204. In some preferred embodiments, the OCR program is available commercially. In preferred embodiments, the OCR engine is supported with a general as well as a business-domain-specific dictionary to increase the accuracy. The OCR output may optionally be in text and xml formats, or may be in other formats.

In preferred embodiments, once a image type document is OCRed, the output file is converted to a grid-based matrix format to form a text-pictorial representation of the document (Document Grid File). Text type documents, such Microsoft word documents are also converted into a Document Grid File.

In preferred embodiment the output from image preprocessing and OCR is used for the Image Quality Detection IQD 205.

Identification:

All pages or documents are placed in buckets using a preliminary analysis of features discovered in the Document Grid File, without detailed validation. The order of the presented pages, sheets, and/or documents presented to the method/apparatus is recorded by the system, for example using a computer database.

In preferred embodiments, the method/apparatus then attempts to identify one or more reference pages and then the documents are grouped logically based on the reference page and/or affinity 206. Numerous classification engines known in the art can be used, separately or together, including a Word Map 207, a SVM classifier 208, a Location Diagram 209, a Bayesian classifier 210, and a critical-feature-based identifier 211, but any manner known to those of skill in the art can be used. In preferred embodiments, the engines are used in a particular order. In some preferred embodiments, if all the classification engines agree as to the classification of a page or document, the result is accepted as the identification, and taxonomy classification, of the document; in other or the same preferred embodiments, if most of the classification engines agree, then the result is accepted as the identification of the document. If the document, page or sheet is not identified at this point, further analysis is performed with the aid of a human collaborator (i. e., via escalation). Further, in preferred embodiments, discontinuities are used to identify pages from a single document, e.g., a sentence or a table separated by a page break.

Preferably, all pages are revisited and checked with regard to their affinity towards the reference page. This method of confirmation in preferred embodiments works by measuring affinity of the pages in the vicinity of a reference page towards that reference page, but also reviews pages far removed (distance measurement) from the reference page to guard against, and correct, pages being shuffled during document assembly of the Dox Package or input into the system. In preferred embodiments, the page “footer” description is measured for closeness, an example of distance measurement, against the reference page using fuzzy logic matching techniques, and other mathematical techniques as known to one of skill in the art.

Taxonomy Classification/Mapping:

In preferred embodiments, each document page is classified into, or sorted into, one of the taxonomy classes, as defined by the MISMO standards committee or pre-programmed by user using the document learner, or a class designated by a human collaborator. If the putative class identified by the system is unknown and it cannot be classified by the system, the document or representation thereof may be stored in a feedback folder for further manipulation. Taxonomy classification is also done in multiple levels to identify class, sub-class, and version of the document. Taxonomy classification is preferably performed using multiple classification engines. All the outputs of the taxonomy classifier may be flagged or designated as one of four types: (1) classified, (2) multi-class, (3) ambiguous, (4) unknown. Document pages flagged as unknown are submitted to an OCR program from a different manufacturer 212, and re-identified 213 using the same Identifier Engines, 207, 208, 209, 210, 211.

The pages or documents thus far classified may be further evaluated automatically. In preferred embodiments, those documents that fall into categories 2 and 3 are forwarded to the verifier.

Verifier:

Documents that are flagged as either multi-class or ambiguous, or both, are routed through the verifier, although any document used in the system may be routed through the verifier. The document verifier performs a very accurate form of location-based checking for verification of class 214.

Further voting and probability algorithms are preferably used to determine the class for the remaining pages. 215.

The Information Sequencing process is used to create a sequence matrix from the information acquired during the previous steps. 216.

The automatic version detection and page sequencing for some or all documents is done using the Versioning and Sequencing engine. 217. This is done using the Feature Vectors specific to versions and sequencing matrix as captured 216.

Classification Exception Handler:

Document pages that are still not mapped to a taxonomy class due to bad image quality, a new variation of a document, or for other reasons that do not result in immediate identification or classification are flagged as Unknown. Document pages that fall below the confidence threshold value that may be preset or varied by the user, even after the verifier, are sent to exception handling client (Classification client) (i.e., via escalation) 218. There, human collaborators can verify the class, assign a class, or note that the document cannot be identified.

If a human collaborator verifies or changes the class, this information is sent to a feedback box for an incremental learning. During escalation, in preferred embodiments, the human collaborator is presented with an image comprising the header and footer region of the page or document in question, and optionally with an image of the entire page if the image quality is poor. Frequently the identification of the page or sheet may be made in reference to the header and footer information, although display parameters, such as position of the various images on the human collaborator's computer screen and zoom capabilities, may be varied by the human collaborator. Escalation may occur before, but preferably occurs after, the verifier step.

Apply Filter for Classification: For documents having peculiar properties, such as a specific variation of a class of documents, a filter may optionally be applied. An example is if two documents are very close in format and data, but they differ in a very specific property and because of that they belong to different class. A weighted filter, that is a Location Diagram with primary key set for the distinguishing property or feature, is applied so that those can be classified accurately and rapidly. This technique is also used for determination of different versions of documents. For example, two notes may have very similar contents but differ in specific feature such as the absence or presence of an interest rate adjustment clause, need to be put in different classes for business decisions involving an Adjustable Rate Note and/or a Fixed Rate Note.

Collating (Class Specific):

Within each taxonomy class as determined to this point, document pages are collated using methods of analysis based on Location Diagrams and Feature Vectors as may be understood by one of skill in the art 219. These methods of analysis determine the sequence, page numbers of pages and sheets within documents. This process of collation is capable of determining not only the class of a document page (which in most cases is determined earlier at the Classification step or the Verifier step) but also the exact identification of a document including the version number of each particular document within the Dox Package. The collation methods also correctly identifies the pagination within the document, and also notes and records the presence of duplicate documents. For example, during collation, the method/apparatus of the instant invention may find and note as identical two identical mortgage notes in a single taxonomy class. This collation process of the instant invention is differentiated from classification technologies known in the art by its ability to distinguish closely related documents. An example of this is that the method/apparatus of the instant invention can pick two mortgage notes out of a Dox Package, correctly paginate them, and identify and log them as separate, but otherwise identical, documents. Pages or documents are then segregated into a logical group determination, and the pages are mapped to a predetermined business-specific or user-determined taxonomy.

In preferred embodiments of the instant invention, the collation process is based on incremental learning and various artificial intelligence (“AI”)-based techniques, which may include one or more of the following, such as:

-   (1) the Location Diagram- and Feature Vector-based feature     extraction and page mapping; -   (2) SVM and NLP; -   (3) an intelligent filter technique taking advantage of header and     footer based information; -   (4) collation by finding common threads within or between pages,     documents, or sets of documents; -   (5) finding disagreements based on affinities; -   (6) inference-based mapping; and -   (7) feature based discontinuity detection and collation, as well as     human collaboration.

The collate confidence matrix which is the result of the above-described collation process is preferably used for final formation of documents. The collate confidence matrix represents affinity among various pages, positions of the pages within sets and the confidence of mapping to a particular taxonomy.

Extraction:

In preferred embodiments of the invention, extraction of information or data from the documents or Dox Package that has been captured using the method or apparatus of the invention, and preferably extraction is first done automatically from readily identifiable fields 221 and image snippets of other fields location are re-submitted to the OCR step with a field specific dictionary before repeating the extraction process 220. Using a Location Diagram-based method allows the location of fields even in case of variation between pages or documents within or between Dox Packages 221. Values missed by automatic extraction of these methods may be located by an automatic field locator 222. The automatic field locator uses auto field location based on Location Diagrams 223 and Image based field locator 224. In preferred embodiments of the instant invention, if automatic field locator cannot locate values, the region of the page and/or sheet in question is escalated and the field may be identified with the assistance of a human collaborator by escalation 225. In preferred embodiments, the human collaborator may be shown only the relevant region of a page or sheet (Image Snippet) and may identify the region containing the data to be extracted by simply mousing over the region with the values extracted by further processing 226 and, in preferred embodiments, the location of the value within the document then sent to the feedback folder for future reference in regard to learning and optimization of the system. In related preferred embodiments, the human collaborator indicates exactly where the field is located.

Relevant information, as defined by a pre-determined business-specific application or set by a user, is extracted from documents that have been successfully classified. In some preferred embodiments, each time a Location Diagram is resolved to select a field region, an overall weight may be associated with that solution and used to improve future selection of fields in a particular class of document.

Scrubbing and Verification of Extracted Information:

Extracted values are scrubbed to get exact value 227. Scrubbing further transforms the extracted value to a specific data type. The accuracy of the scrubbed value is verified. Thus, the system provides multiple confidence levels for decision-making. The system generates a Knowledge Object from the scrubbed results. The values with very high extraction confidence but very low scrubbing confidence are sent to a human assisted Field Location Process (“Manual FLP”) 225. The system generates a field value from scrubbed results that pass the confidence threshold for the overall process.

Extraction Exception Handler:

Extracted data falling below confidence threshold value is sent to exception handling client (Manual Extraction Process (“MEP”)) (i.e., via escalation) 225 & 226. Human collaborators can verify and/or change the data and/or extracted information in reference to the Dox Package. In preferred embodiments, each field subjected to MEP is extracted by a minimum of two human collaborators and the system compares the extracted value. In the event of a discrepancy, the value in question can be sent to additional human collaborators.

In all the steps involving human collaboration, the method/apparatus of the instant invention may optionally keep track of which data was viewed by human collaborators, and how long they viewed the data, in order to detect potential fraud or illicit activities. Information related to exceptions may also be used for statistical learning. In preferred embodiments, the human collaborator mouses over the exact value to be extracted. This is referred to herein as a “snippet” or a “text snippet” and the method/apparatus can pull the snippet and subject it to further scrubbing and processing 227. These snippets of required/specific values may also be extracted and used for formation of Knowledge Objects.

Transformation:

The processes preferred embodiments of the instant invention typically extracts the fields (as they appear in the document) required for various business and/or compliance requirements, then transforms them into facts that can be used further for decision making by an automated rules engine or search engine by packaging these facts and other related information such as text and image snippets, x,y coordinate location of these facts from a Location Diagram into an entity referred to herein as a Knowledge Object. A Knowledge Object 228 is an information matrix with the relationship among all the information entities clearly defined 229.

Knowledge Objects can be used to form Business Objects. A Business Object is a collated set of Knowledge Objects created for use in particular business context such as a MISMO SMART Doc XML file, custom transaction set or electronic document. Business Objects give data a business centered view of the information captured by the method/apparatus. 230 Business Objects are stored and used for business decision making by a Decision Engine. These Knowledge Objects and Business Objects are stored in an electronic data repository which can further be used by a decision engine, 231 a rule engine, or a search engine to make various decisions and/or accelerate, support, or validate decisions.

Further Features

Business Object Formation:

FIG. 4 depicts Business Object formation. The relationships among all Knowledge Objects is established by a method called Collation 401. The output of collating Knowledge Objects is done by referring to a knowledge map which has a business-process-specific knowledge representation of the Business Object required for making business decisions 402. For example a organized Dox Package in the form of a MISMO SMART Doc 403, XML representations of industry standard documents 404 405 406 Industry standard transaction sets defined by MISMO 407 408 409.

FIG. 4A depicts the relationships among the Knowledge Objects. The relationship between Knowledge Object P1 410 and Knowledge Object P2 411 is shown in the figure. I1 and I3 is the set of common features belonging to P1 and P2, I2 is the set of data elements, I4 is set of location co-ordinates (snippets and regions) and I5, I16, I17, and I8 are the other attributes of P2. Since I1 and I3 are common to P1 and P2, the knowledge map is referenced to determine if they have affinity to the same category of Business Object such as a Promissory Note.

As an example FIG. 4B depicts the process of Dox Package creation. Here Document-1, Document-2, Document-n 412 413 414 have their individual attributes. (Attributes from left to right 415 416.) Based on these attributes these documents are mapped to the taxonomy. Here the collation process is used to determine affinity to a Dox Package based on common attributes such as Loan number or borrower name.

FIG. 4C depicts the process of Document formation. Here pages page-1 to page-n 417 418 419 420 421 based on closeness among pages, Feature Vectors and affinity 422 are mapped to different documents and their copies, revisions.

FIG. 4D depicts a Business Object MISMO AU.S. Transaction set 428. The Knowledge Objects extracted from various forms like 1003 429, 1004 434, and Note 433 are combined to form a transaction set for underwriting of a loan using a rules engine.

Incremental Learning:

The system of preferred embodiments of the instant invention performs incremental learning and tuning based on feedback and/or unclassified documents. All Feature Vectors are retuned without actual calculation of relative distances. The incremental learning is based on statistical analysis of exception and tuning.

The system keeps watch on statistical data of the collate, classification and extraction to dynamically tune various control parameters and optimize results. Further, in preferred embodiments, the method/apparatus can readily keep track of where human collaborators reviewed data and how long they accessed the data, thus enabling an operator of the instant invention a certain level of protection against fraud.

Learning:

FIG. 3 depicts the flow of learning in one embodiment of the instant invention. The document samples for the document to be learned, and document-specific dictionaries and generic, as well as domain, dictionaries are loaded in to the Learner's Knowledge Base. 301. The Learner reads the document samples, and if document specific dictionaries are not available, then one is generated from the sample documents. 302. For some specific files, human input such as very specific key phrases and location are provided for learning, if required. 303 Text Feature Vectors are created using image processing, machine learning and Location Diagram based techniques and other methods known in a manner known to those of skill in the art. Here the Feature Vector represents various text features including frequencies, relative locations and Location Diagrams. 304. The distances among the Feature Vectors representing different classes, locating different information are maximized. Weights are assigned to Feature Vectors based on their uniqueness and distance from the other Feature Vectors. 305. Using Statistical techniques thresholds are calculated. 306. If the Feature Vectors uniquely identify document 307 document is flagged and Feature Vectors are loaded 308. Otherwise the Feature Vector is re-tuned to prevent misclassification by maximize the distance to from the wrongly classified document class 309. Similarly Feature Vectors are created based on image features. 310. These sets of Feature Vectors are then mapped to a class. 311. The Feature Vectors are tuned to optimize the results. 312. The documents are flagged and corresponding Feature Vectors are loaded in the system. 313. The text and image based learning process complements each other and can be performed in any order. The output of the learner is a collection of reference-sets that are then stored in a Knowledge Base of the Classifier and Extractor methods to reference.

Regarding Classification:

The system can prepare reference-sets of known classes with title of the class, i.e., taxonomies. The system can use either a dictionary specific to the endeavor domain (i.e., real estate) or a dictionary specific to a document classes.

The system can, based on reference-sets, generate a dictionary for each class. This dictionary also contains a weight for each word. The weight for each word plus a weight for combinations of words is determined based on frequency and Bayesian analysis of word features with reference to document identity.

Learning also generates Feature Vectors based on Location Diagrams for each set (reference-set). The Feature Vectors generated represent precisely that set of documents, or at least most of the documents, in that reference-set.

The method/apparatus can maximize distance between Feature Vectors derived from Location Diagrams to eliminate overlap and give high weight to properties those are specific to the document.

The method/apparatus can also load Feature Vectors from an outside source.

To address the needs of assigning a unique position to each page in a set of documents to its business context, as well as other needs associated with the given business, the instant invention features in one embodiment a method/apparatus that identifies and collates individual documents and revisions of the same document type within a set. The method/apparatus automatically identifies discontinuities using various algorithms to identify groups or logical units of documents. The instant invention takes advantage of its computer and human collaboration and to utilize the strengths of both. The output of the method/apparatus is a Business Object like MISMO Smart Docs. The Business Object is a business-centered Knowledge Object representation useful to a business decision maker. Further, the method/apparatus of preferred embodiments of the instant invention has a method for making decisions based on business processes to select and organize the Business Objects and provide automated decisions in some situations. The Business Object contains a complete collated and bucketed set of documents, complete relationships of KOs for specific process, etc. Further collated documents and information is presented with the business identification furnished and mapped to the business-specific or user-provided taxonomy. Further this method/apparatus measures relatedness among various pages and sorts and identifies documents on the principle of the reference page.

The instant invention, in preferred embodiments, collates pages from the input set of documents into a logical/numerical sequence. The fields required for different business processes are extracted from these collated and taxonomy-mapped buckets. In preferred embodiments, the instant invention also provides for fact transformation so that the information extracted from the pages in the document set is converted into usable form and can be used directly according to various business-specific manners. The instant invention, in preferred embodiments, provides the formation of Knowledge Objects and additionally ready-to-use Business Objects.

The processes of the instant invention, in preferred embodiments, typically extract the information fields required for various business and/or compliance requirements, then transforms them into facts that can be used further for decision making. The decision system used for analyzing the document set provides logical decisions based on the information within and the business context. The instant invention offers a collation system and complete organization and fact extraction solution that forms the information matrix, Knowledge Objects. This allows information flow from paper documents from a wide variety of types of images to decision-making based on error free analysis using the techniques of intelligent mapping available to the operator of the instant invention. The invention is highly scalable because of its dynamic learning ability based on Feature Vectors and ability to create Business Object based on requirement and business process.

The applications for this Business-Object-creation based on Knowledge Objects as are created by processes such as intelligent document collation and extraction of information are not limited to the mortgage and insurance industries. In fact, this method is useful where there is any business process that uses information from unorganized set of documents. All the places where unorganized information from the documents need to be used for business decisions this business method is useful. It can be used for Knowledge Object creation based on information extraction from various sources of images, paper documents, and PDF files. Further, this system can be great help for many processes, both inside the legal field and otherwise, that are based on signed documents and files with information available within the set of documents is distributed across a variety of pages.

Thus, some preferred embodiments of the instant invention feature:

-   (1) output of data from Dox Packages as Business Objects (e.g. MISMO     SMART Doc) that is business-type specific (Underwriting, Servicing,     Closing process etc.); -   (2) a complete process right from information/document capture to     creation of Business Objects which can directly used for automated     decision making and also to advise manual decisions; -   (3) unique flow with new algorithms; -   (4) novel, user-adjustable, and very business-specific     representation of information; and/or -   (5) making data, or rather the Business Object, available to make     facilitate e-mortgage processing as envisioned by MISMO.

In some embodiments, one of the major purposes of the process of the instant invention is Knowledge Object and Business Object creation. The final output is a Business Object and not only a set of classified or sorted documents. Further, in some embodiments, the purpose of the instant invention is not classification or extraction but to create Business Objects like MISMO Smart-Docs from Knowledge Objects, thereby accelerating automated and manual business decisions.

The basic method used for classification is different from current methods. Also the manner in which and sequence the instant invention uses various complementary technologies, such as filtering and voting, makes the method of the instant invention more accurate.

Additionally, the flow of preferred embodiments of the instant invention is uniquely valuable in yielding Business Objects. Various algorithms are used in a manner and sequence to obtain optimal accuracy. Also, the process of preferred embodiments of the instant invention emphasizes feature/knowledge extraction out of Dox Packages with classification and document separation an allied output. The instant invention, in preferred embodiments, locates the knowledge portion within a Dox Package irrespective of slippage and page numbers. Thus, the instant invention may provide information for the downstream business process directly from Dox Package capture to Business Object creation and decision-making based on the Business Objects.

The assembly of technology and algorithms unique to the instant invention in some embodiments may include at least some, or all of, the following in preferred embodiments:

-   (1) The intelligent information locator of the instant invention may     help the business process by locating the business critical     information. The location algorithm uses a novel method to provide     accuracy. -   (2) The method of preferred embodiments of the instant invention     identifies all available sources and multiple occurrences of the     same information across the Dox Package, i. e., to different     versions of the same type document; this enables the user to compare     this information and make decisions based on the most recent or     relevant information. -   (3) The image- and text-based information locator of the instant     invention, in preferred embodiments, takes advantage of image and     text properties of the documents while locating the information. -   (4) The instant invention recognizes that the document boundaries in     business context are not as significant as the multiple occurrences     and sets of Knowledge Objects that suggest the presence of more than     one form of the same types. -   (5) The information locator may also indicate versions and     facilitates relevant decisions. -   (6) The Location Diagram-based method may be used for rapid location     of data, and, which in turn, returns the data association with the     image. -   (7) The Location Diagram based method locates may collect     information from proper page irrespective of similarities among the     pages, as well as new variations among the forms. -   (8) The Location Diagram-based locator can locate appropriate     information based on the version of the form. -   (9) The Image and Location Diagram based locator can locate the     information on forms irrespective of poor quality of images/OCR     output. -   (10) The system of the instant invention either may extract or make     available the relevant portion or the Dox Package for knowledge     extraction by an operator by increasing extraction efficiency by up     to 5X over prior methods. -   (11) The instant invention, in preferred embodiments, features less     turnaround or learning time. -   (12) The instant invention, in preferred embodiments, features     incremental learning as to locations. -   (13) The instant invention, in preferred embodiments, features     automatic and semiautomatic learning for added flexibility. -   (14) The instant invention, in preferred embodiments, features the     verifier for verifying location. -   (15) The instant invention, in preferred embodiments, features a     scrubber which can scrub extraction output. -   (16) The instant invention, in preferred embodiments, features the     ability of establishing knowledge-based relationship among all the     relevant knowledge portions resulting in a rich Knowledge Object     that can help in Creation of Business Objects. -   (17) The instant invention, in preferred embodiments, features     collation of Knowledge Objects to create Business Objects. -   (18) The instant invention, in preferred embodiments, features     efficient decision making based on Business Objects.

EXAMPLES

FIG. 5 depicts screen shots of output obtained through the use of one embodiment of the instant invention. That is, using a Dox Package analyzed by the method/apparatus as described herein, the following exemplary data was available for analysis for making business decisions.

The invention illustratively described herein can suitably be practiced in the absence of any element or elements, limitation or limitations that is not specifically disclosed herein. Thus, for example, the terms “comprising,” “including,” “containing,” etc. shall be read expansively and without limitation. Additionally, the terms and expressions employed herein have been used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalent of the invention shown or portion thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Thus, it should be understood that although the present invention has been specifically disclosed by preferred embodiments and optional features, modifications and variations of the inventions embodied herein disclosed can be readily made by those skilled in the art, and that such modifications and variations are considered to be within the scope of the inventions disclosed herein. The inventions have been described broadly and generically herein. Each of the narrower species and subgeneric groupings falling within the generic disclosure also form the part of these inventions. This includes within the generic description of each of the inventions a proviso or negative limitation that will allow removing any subject matter from the genus, regardless or whether or not the material to be removed was specifically recited. In addition, where features or aspects of an invention are described in terms of the Markush group, those schooled in the art will recognize that the invention is also thereby described in terms of any individual member or subgroup of members of the Markush group. Further, when a reference to an aspect of the invention lists a range of individual members, as for a non-limiting example, ‘the letters A through F, inclusive,’ it is intended to be equivalent to listing every member of the list individually, that is ‘A, B, C, D, E and/or F,’ and additionally it should be understood that every individual member may be excluded or included in the claim individually. Additionally, when a reference to an aspect of the invention lists a range of individual numbers, as for a non-limiting example, ‘0.25% to 0.35%, inclusive,’ it is intended to be equivalent to listing every number in the range individually, and additionally it should be understood that any given number within the range may be included in the claim individually.

The steps depicted and/or used in methods herein may be performed in a different order than as depicted and/or stated. The steps are merely exemplary of the order these steps may occur. The steps may occur in any order that is desired such that it still performs the goals of the claimed invention.

From the description of the invention herein, it is manifest that various equivalents can be used to implement the concepts of the present invention without departing from its scope. Moreover, while the invention has been described with specific reference to certain embodiments, a person of ordinary skill in the art would recognize that changes can be made in form and detail without departing from the spirit and the scope of the invention. The described embodiments are considered in all respects as illustrative and not restrictive. It should also be understood that the invention is not limited to the particular embodiments described herein, but is capable of many equivalents, rearrangements, modifications, and substitutions without departing from the scope of the invention. Thus, additional embodiments are within the scope of the invention and within the following claims. 

1. A method of doing business by processing a group of Dox Packages from the mortgage industry wherein each Dox Package has at least two pages wherein minimal human intervention is involved.
 2. A method of doing business by processing a group of Dox Packages from the mortgage industry wherein each Dox Package has at least two pages wherein the information is extracted from the Dox Packages and organized ten times as fast as a human operator.
 3. A method of doing business by processing a group of documents comprising the steps of: (1) performing optical character recognition from said discrete documents using said device to generate one or more sets of text-based information; (2) classifying at least some of said discrete documents using said sets of text-based information, wherein multiple classification engines are employed and said classifying is based on a consensus of said classification engines; (3) classifying at least some of the discrete documents using Image Based Classification; (4) verifying any of said remaining discrete documents that are not classified in said steps of classifying by employing a Location Diagram with said remaining discrete documents or a portion thereof; (5) collating said at least two of said discrete documents; (6) versioning and sequencing at least two of said discrete documents; (7) locating said fields containing data in said at least two of said discrete documents; (8) extracting data from said fields of said at least two discrete documents to generate extracted data; (9) scrubbing values from said extracted data to generate values therefrom; (10) forming Knowledge Objects; (11) storing said values in a data storage device; (12) forming Business Objects; (13) displaying at least some of said values to a user.
 4. The method of claim 1, wherein said step of performing optical character recognition is performed by, or with the assistance of, a computer.
 5. The method of claim 1, wherein said step of classifying is performed by, or with the assistance of, a computer.
 6. The method of claim 5, wherein said step of classifying is performed by Image Based Classification.
 7. The method of claim 1, wherein said step of verifying is performed by, or with the assistance of, a computer.
 8. The method of claim 1, wherein said step of collating is performed by, or with the assistance of, a computer.
 9. The method of claim 1, wherein said step of versioning and sequencing is performed by, or with the assistance of, a computer.
 10. The method of claim 1, wherein said step of extracting data is performed by, or with the assistance of, a computer.
 11. The method of claim 1, wherein said step of scrubbing is performed by, or with the assistance of, a computer.
 12. The method of claim 1, wherein said step of formation of Knowledge Object is performed by, or with the assistance of, a computer.
 13. The method of claim 1, wherein said step of outputting is performed by, or with the assistance of, a computer.
 14. The method of claim 1, wherein said step of formation of Business Object is performed by, or with the assistance of, a computer.
 15. The method of claim 1, wherein said step of displaying is performed by, or with the assistance of, a computer.
 16. The method of claim 1, wherein ambiguities at any step are escalated to a human operator.
 17. The method of any of claim 1, wherein an ambiguity in a classification step, a field location step, or a data extraction step are escalated to a human collaborator.
 18. The method of any of claims 2-16, wherein said group of documents is a group of mortgage loan documents. 